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Data Governance Handbook

Name: Data Governance Handbook
Author: Wendy S. Batchelder
ISBN: 9781803240725

by Wendy S. Batchelder

May 2024

Beginner to intermediate

396 pages

11h 35m

English

Packt Publishing

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Data Governance Handbook
Editorial ReviewsContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat you will learn:What this book coversTo get the most out of this bookGet in touchShare Your ThoughtsMaking the Most Out of This Book – Get to Know Your Free BenefitsStep 3
Part 1:Designing the Path to Trusted Data
Chapter 1: What Is Data Governance?
What you can expect to learnWhat’s driving the increasing need for data governance?What is data governance?What data governance is notThe objective of data governance – create business valueA brief overview of the data governance componentsPolicy and standardsRoles and responsibilitiesGovernance forumsReporting on governance progressRelated teams and capabilities needed for successDefining valueWho to meet withCrafting a powerful why statementCustomizing the messageData governance as a strategic enablerThe mission of the chief data and analytics officeThe mission of the data governance programBuilding a business case for your companyWhen and why to launch a data governance programWhy you should launch nowWhy you might want to waitHow to build your delivery timelineConclusionReferences
Chapter 2: How to Build a Coalition of Advocates
Building relationships with impactBuilding trust one relationship at a timeIdentifying stakeholdersBuilding a stakeholder mapThe case for building trust in dataLanding an executive sponsorIdentifying and assessing sponsorsBuilding a business case to land a sponsorA note on translating to business outcomesEstablishing feedback loopsKey roles to support youHow to gain the support of the massesConclusionReferences
Chapter 3: Building a High-Performing Team
Optimizing for outcomesCommon outcomesDefining core functionsIncorporating product management in organizational designThree common data organization modelsEstablishing the office of the CDOMaturing and empowering through the hub and spoke modelDriving consistency through the centralized modelHow to select the right model for your organizationWhat roles are neededCDO versus CDAOData management rolesData solutions leaderAI considerationsHow to structure the team for results (and why)Building the rhythm of the business of dataEnterprise data committeeEnterprise data councilFunctional rolesExecutive data domain leaderBusiness data stewardTechnical data stewardTalent developmentRecruiting talentGrowing the pipeline of talentUpskilling and reskillingConclusionReferences
Chapter 4: Baseline Your Organization
What is a data management maturity model?Overview of processWhy you should baseline data management maturityFoundational reasons to baselineExecuting a data management maturity assessment[#1] Defining the scope[#2] Identifying stakeholders[#3] Selecting a data management maturity model[#4] Execute the assessment and collect data[#5] Analyzing the dataAlignment and agreement[#6] Communicate the resultsCommunicating disaggregated resultsCommunicating aggregated resultsProgram baseline[#7] Develop a plan[#8] Implement the plan[#9] Monitor progress[#10] Reassess your maturityMeasuring successConclusion
Chapter 5: Defining Success and Aligning on Outcomes
Capabilities versus outcomesCapabilitiesOutcomesBusiness outcomes and data capabilitiesYou need bothWhat is success?What is the definition of value?Defining successAligning on outcomesStep 1 – Aligning on the business outcomeStep 2 – Defining data capabilitiesStep 3 – Defining data capability deliverablesStep 4 – Aligning on value measurementStep 5 – Delivering iterativelyStep 6 – Reporting on progress iterativelyStep 7 – Measuring success in data outcomesStep 8 – Measuring success in business outcomesSummaryBarriers to achieving business valueBuilding value measures into your stakeholder mapConclusion
Part 2:Data Governance Capabilities Deep Dive
Chapter 6: Metadata Management
Metadata management definedWhat is metadata management?The value of metadata managementWhy does metadata matter?Core metadata capabilitiesMetadata standardsBusiness glossaryData catalogBuilding optimal metadata management capabilityWhat is a data marketplace?What’s in a data marketplace?Why does a data marketplace matter?Measuring outcomes and return on investmentSetting up metadata management for successConclusionReferences

Chapter 7: Technical Metadata and Data Lineage
Technical MetadataWhy does it matter? What matters?How do you measure the value?Which metrics should be used to measure maturity?Who manages it?What does maturity look like?How should you use it?Data LineageWhy does it matter? What matters?How do you measure the value?What metrics should be used to measure maturity?Who manages it?What does maturity look like?How should you use Data Lineage?Building an optimal Data Lineage capabilityConclusion
Chapter 8: Data Quality
Data quality definedData Quality StrategyData quality enablementThe value of measuring data qualityCore capabilitiesData profilingData cleansingData validation and standardizationData enrichmentFeedback loops, exception handling, and issue remediationBuilding an optimal data quality capabilityCertified dataTransparencySetting up data quality for successThe real-time requestIntegrations with other systemsConclusion
Chapter 9: Data Architecture
Data architecture definedSimple winsThe value of data architectureWhy data architecture is often overlookedMeasures of successCore capabilitiesEstablishing a data architecture programAs-is and to-be modelingBuilding an optimal data architecture capabilityEstablishing design principlesDeveloping architectural standardsTight integration with business architecture and IT architectureBuilding data architecture into the systems development life-cycle processSetting up data architecture for successConclusion
Chapter 10: Primary Data Management
Defining Primary Data ManagementReference DataPrimary Data versus Reference DataTypes of Primary DataCustomerProductVendor [or Supplier]ContactBuilding an Optimal Primary Data Management Capability: Core Capabilities for SuccessDe-duping or DeduplicationCommon DefinitionsGolden Source AttributionHierarchiesTrust LogicIntegrationQuality Third-Party EnrichmentConsumption ModelCRM vs. PDMWhat is CRM?Key DifferencesThe Value of Primary Data ManagementBuilding the Business CaseA Note on Scope of ProgramCapability StatementsConceptual ArchitectureDirectional Objectives & Specific Measures of SuccessBusiness Benefits of PDMConclusionReferences
Chapter 11: Data Operations
Defining data operationsData operations versus IT operationsIT and data operations partnershipsData operations capabilitiesThe value of data operationsThe unsung hero of data governanceMaking data operations more visibleBuilding an optimal data operations capability and setting up for successConclusion
Part 3:Building Trust through Value-Based Delivery
Chapter 12: Launch Powerfully
Assessing readiness for launchPerforming the assessmentCommon baselineSimple and strong core messagingCrafting a compelling visionAs Is versus To Be (aka current versus future state)Getting crisp with your messagingWriting a narrative memoDesign based on outcomesCreating a repeatable processDesigning feedback loopsSetting and meeting expectations in the program launchConclusion
Chapter 13: Delivering Quick Wins with Impact
Finding quick winsIdentifying areas of needRationalizing the listPrioritizing the listShort-term versus long-term winsOrganizational readiness considerationsInvestment/funding modelsFollow throughCommunicate effectively for supportWhy policies, standards, and procedures can generate buzzData ownershipApplying a product mindset to data capabilitiesProduct management for dataProducts versus non-product solutionsBuilding momentum through a continuous delivery modelContinuous delivery modelFollow throughConclusionFurther reading
Chapter 14: Data Automation for Impact and More Powerful Results
What is automation?What is data automation?Types of data automationAdvanced data automation capabilitiesBenefits of data automationMeasuring the benefitsHow to determine which type of automation to useStep 1 – Identify your goalsStep 2 – Identify the existing process and pain pointsStep 3 – Agree on the problem statement(s)Step 4 – Align on the approach and ROI calculationStep 5 – ExecuteStep 6 – Measure and reportThird-party enrichmentData solution examples powered by data automationCustomer domainOperations domainConclusion
Chapter 15: Adoption That Drives Business Success
Why adoption matters – getting startedStart with the whyAdjust the solution (if needed) and make it easy to useDon’t forget about cultureAddress barriers to adoptionLow adoption is costlyQuantitative costs of low adoptionQualitative costs of low adoptionWhy does adoption fail?The solution is the problemYour company is the problemYou are the problemHow to succeed at driving exceptional adoptionRecovering from failed launchesUncover the root problemCollaboration (almost always) winsPost-deploymentAdoption roadmapMonitoring activitiesBaking adoption into SDLC practicesConclusion
Chapter 16: Delivering Trusted Results with Outcomes That Matter
How to message stakeholdersFocus on value and impactSpeak their languageAddress concerns and build trustUse clear and compelling communicationHow to communicate unexpected results and variances from commitmentsOffer clarity and contextFocus on solutions and next stepsMaintain transparency and open communicationHow to deliver results to build trustPrioritize collaboration and communicationDemonstrate expertise and competenceFoster a culture of openness and accountabilityCapability reviewData governanceMetadata (business and technical)Data qualityData architectureData operationsConclusion
Part 4:Case Study
Chapter 17: Case Study – Financial Institution
Scenario - highly regulated entity – banking institutionIdentifying quick winsInitial discoveryKey themesQuick winsMessaging long-term solutions to the executive teamMessaging to the regulatorsHow to design for iterative delivery with impactResultsConclusion
Index
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Overview

"Data Governance Handbook: A practical approach to building trust in data" equips data professionals with the necessary tools to master data governance. Written by Wendy S. Batchelder, an experienced executive, this book provides actionable guidance to launch effective governance programs that deliver measurable business outcomes. Discover practical insights and strategies from real-world experiences.

What this Book will help me do

Understand the fundamentals of data governance and its essential components.
Learn to effectively align data governance initiatives with business strategy.
Develop the ability to gain executive buy-in and support for governance programs.
Master techniques to ensure adoption and success of governance initiatives.
Harness real-world examples to drive impactful and measurable results in your organization.

Author(s)

Author Wendy S. Batchelder is a seasoned data governance expert who has held the role of Chief Data Officer at leading Fortune 500 companies. With her extensive experience, she combines technical expertise with practical insights. Wendy's approach focuses on creating scalable, impactful solutions, making complex concepts accessible to data professionals at all levels.

Who is it for?

This book is ideal for data professionals such as Chief Data Officers, data governance leaders, and IT specialists who aim to enhance their skills in data governance. Whether you're starting or looking to refine your approaches, this comprehensive guide helps you create business-aligned solutions. A basic understanding of data concepts is recommended, but no advanced tech skills are required.

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